Recombination operators and selection strategies for evolutionary Markov chain Monte Carlo algorithms
From MaRDI portal
Publication:613018
DOI10.1007/S12065-010-0040-1zbMath1214.68282DBLPjournals/evi/DruganT10OpenAlexW1988382099WikidataQ41731122 ScholiaQ41731122MaRDI QIDQ613018
Dirk Thierens, Madalina M. Drugan
Publication date: 17 December 2010
Published in: Evolutionary Intelligence (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc2987561
Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Numerical analysis or methods applied to Markov chains (65C40)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Optimization by Simulated Annealing
- Parallel recombinative simulated annealing: A genetic algorithm
- Parallel and interacting Markov chain Monte Carlo algorithm
- A new connection between quantum circuits, graphs and the Ising partition function
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- An introduction to MCMC for machine learning
- Population Markov chain Monte Carlo
- Sequential Monte Carlo Methods in Practice
- Approximating discrete probability distributions with dependence trees
- Monte Carlo sampling methods using Markov chains and their applications
- Artificial Evolution
- A survey of optimization by building and using probabilistic models
This page was built for publication: Recombination operators and selection strategies for evolutionary Markov chain Monte Carlo algorithms